Some tips about uncertainty visualization and associated techniques.
Key ideas
The data associated with each point is a distribution instead of a fixed value
Source of the uncertainty
looking at related ensmeble paper, compression, noise, and ensmeble simulation.
Typically, the uncertainty can come from noise, the down-sampling, and ensmeble members generated by simulation. The
monte carlo sampling
using to distribution to sample from dedicated one
using sampled number dividid by whole number, using semi monte-carlo way, sample, then compute the probability
density estimation
put the para non-para table here
the covaraince matrix.
The idea is simple, given an sampled array, how to derive its original distribution.
All kinds of ideas are summaries in the section of density estimation for the blog with title “Many aspects of MG, GMM and implementing linear algorithm”
Combine with the ml
gaussian splitting
Discriminative network
